标签：java 18 doc1 doc 搜索 position phrase spark match
1、什么是近似匹配
两个句子
java is my favourite programming language, and I also think spark is a very good big data system.
java spark are very related, because scala is spark's programming language and scala is also based on jvm like java.
match query，搜索java spark
{
"match": {
"content": "java spark"
}
}
match query，只能搜索到包含java和spark的document，但是不知道java和spark是不是离的很近
包含java或包含spark，或包含java和spark的doc，都会被返回回来。我们其实并不知道哪个doc，java和spark距离的比较近。如果我们就是希望搜索java spark，中间不能插入任何其他的字符，那这个时候match去做全文检索，就不行了。
如果我们要尽量让java和spark离的很近的document优先返回，要给它一个更高的relevance score，这就涉及到了proximity match，近似匹配
如果说，要实现两个需求：
1、java spark，就靠在一起，中间不能插入任何其他字符，就要搜索出来这种doc
2、java spark，但是要求，java和spark两个单词靠的越近，doc的分数越高，排名越靠前
phrase match：短语匹配 ，proximity match：，近似匹配
这一讲，要学习的是phrase match，下一讲会讲proximity match，
phrase match就是仅仅搜索出java和spark靠在一起的那些doc，比如有个doc，是java use’d spark，不行。必须是比如java spark are very good friends，是可以搜索出来的。
就是要去将多个term作为一个短语，一起去搜索，只有包含这个短语的doc才会作为结果返回。不像是match，java spark，java的doc也会返回，spark的doc也会返回。
2、match_phrase
修改doc5的content，包含 java spark
POST /forum/article/5/_update
{
"doc": {
"content": "spark is best big data solution based on scala ,an programming language similar to java spark"
}
}
响应结果
{
"_index": "forum",
"_type": "article",
"_id": "5",
"_version": 9,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
}
}
将一个doc的content设置为恰巧包含java spark这个短语
使用match
GET /forum/article/_search
{
"query": {
"match": {
"content": "java spark"
}
}
}
响应结果
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.68640786,
"hits": [
{
"_index": "forum",
"_type": "article",
"_id": "2",
"_score": 0.68640786,
"_source": {
"articleID": "KDKE-B-9947-#kL5",
"userID": 1,
"hidden": false,
"postDate": "2017-01-02",
"tag": [
"java"
],
"tag_cnt": 1,
"view_cnt": 50,
"title": "this is java blog",
"content": "i think java is the best programming language",
"sub_title": "learned a lot of course",
"author_first_name": "Smith",
"author_last_name": "Williams",
"new_author_last_name": "Williams",
"new_author_first_name": "Smith"
}
},
{
"_index": "forum",
"_type": "article",
"_id": "5",
"_score": 0.68324494,
"_source": {
"articleID": "DHJK-B-1395-#Ky5",
"userID": 3,
"hidden": false,
"postDate": "2021-11-11",
"tag": [
"elasticsearch"
],
"tag_cnt": 1,
"view_cnt": 10,
"title": "this is spark blog",
"content": "spark is best big data solution based on scala ,an programming language similar to java spark",
"sub_title": "haha, hello world",
"author_first_name": "Tonny",
"author_last_name": "Peter Smith",
"new_author_last_name": "Peter Smith",
"new_author_first_name": "Tonny"
}
}
]
}
}
单单包含java的doc也返回了，不是我们想要的结果
使用match_phrase语法
GET /forum/article/_search
{
"query": {
"match_phrase": {
"content": "java spark"
}
}
}
响应结果
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.5753642,
"hits": [
{
"_index": "forum",
"_type": "article",
"_id": "5",
"_score": 0.5753642,
"_source": {
"articleID": "DHJK-B-1395-#Ky5",
"userID": 3,
"hidden": false,
"postDate": "2021-11-11",
"tag": [
"elasticsearch"
],
"tag_cnt": 1,
"view_cnt": 10,
"title": "this is spark blog",
"content": "spark is best big data solution based on scala ,an programming language similar to java spark",
"sub_title": "haha, hello world",
"author_first_name": "Tonny",
"author_last_name": "Peter Smith",
"new_author_last_name": "Peter Smith",
"new_author_first_name": "Tonny"
}
}
]
}
}
成功了，只有包含java spark这个短语的doc才返回了，只包含java的doc不会返回
3、term position
doc1：hello world, java spark
doc2：hi, spark java
建立倒排索引并会记录这个关键字所在的position
hello doc1(0)
wolrd doc1(1)
java doc1(2) doc2(2)
spark doc1(3) doc2(1)
了解什么是分词后的position
GET _analyze
{
"text": "hello world, java spark",
"analyzer": "standard"
}
响应结果
{
"tokens": [
{
"token": "hello",
"start_offset": 0,
"end_offset": 5,
"type": "<ALPHANUM>",
"position": 0
},
{
"token": "world",
"start_offset": 6,
"end_offset": 11,
"type": "<ALPHANUM>",
"position": 1
},
{
"token": "java",
"start_offset": 13,
"end_offset": 17,
"type": "<ALPHANUM>",
"position": 2
},
{
"token": "spark",
"start_offset": 18,
"end_offset": 23,
"type": "<ALPHANUM>",
"position": 3
}
]
}
4、match_phrase的基本原理
索引中的position，match_phrase
doc1：hello world, java spark
doc2：hi, spark java
建立倒排索引并会记录这个关键字所在的position
hello doc1(0)
wolrd doc1(1)
java doc1(2) doc2(2)
spark doc1(3) doc2(1)
java spark 使用 match phrase搜索
java spark -->相当于搜索 java和spark
java --> doc1(2) doc2(2)
spark --> doc1(3) doc2(1)
要找到每个term都在的一个共有的那些doc，就是要求一个doc，必须包含每个term，并且java 的position比spark 的position小1，才能拿出来继续计算
doc1 --> java和spark --> spark position恰巧比java大1 --> java的position是2，spark的position是3，恰好满足条件
doc1符合条件
doc2 --> java和spark --> java position是2，spark position是1，spark position比java position小1，而不是大1 --> 光是position就不满足，那么doc2不匹配
必须理解这块原理！！！！
因为后面的proximity match就是原理跟这个一模一样！！！
标签：java,18,doc1,doc,搜索,position,phrase,spark,match 来源： https://blog.csdn.net/m0_37450089/article/details/121442993
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